# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations import errno import inspect import logging import os import pickle import sys import warnings from typing import TYPE_CHECKING, Any, TypedDict, overload import numpy as np import paddle from paddle.base import ( Program, Variable, core, default_main_program, unique_name, ) from paddle.base.executor import Executor, global_scope from paddle.base.framework import ( Parameter, dygraph_not_support, in_pir_mode, process_type_promotion, static_only, ) from paddle.base.log_helper import get_logger from paddle.framework.io_utils import ( _clone_var_in_block_, _load_program_scope, _pack_loaded_dict, _pickle_loads_mac, _unpack_saved_dict, is_belong_to_optimizer, is_parameter, is_persistable, ) from .io_utils import ( _check_args, _check_vars, _get_valid_program, _normalize_path_prefix, _safe_load_pickle, ) from .pir_io import ( get_pir_parameters, load_inference_model_pir, load_pir, load_vars_pir, normalize_pir_program, save_inference_model_pir, save_pir, save_vars_pir, ) if TYPE_CHECKING: from collections.abc import Callable, Sequence import numpy.typing as npt from typing_extensions import NotRequired, Unpack from paddle import Tensor class _NormalizeProgramKwargs(TypedDict): skip_prune_program: NotRequired[bool] class _SerializeProgramKwargs(TypedDict): program: NotRequired[Program] legacy_format: NotRequired[bool] class _SerializePersistablesKwargs(TypedDict): program: NotRequired[Program] class _SaveInferenceModelKwargs(TypedDict): program: NotRequired[Program] clip_extra: NotRequired[bool] legacy_format: NotRequired[bool] class _LoadInferenceModelKwargs(TypedDict): model_filename: NotRequired[str] params_filename: NotRequired[str] class _SaveKwargs(TypedDict): pickle_protocol: NotRequired[int] __all__ = [] _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s' ) def _clone_var_in_block(block, var): assert isinstance(var, Variable) if var.desc.type() == core.VarDesc.VarType.DENSE_TENSOR: return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True, ) else: return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, persistable=True, ) def prepend_feed_ops( inference_program: Program, feed_target_names: Sequence[str], feed_holder_name: str = 'feed', ) -> None: if len(feed_target_names) == 0: return global_block = inference_program.global_block() feed_var = global_block.create_var( name=feed_holder_name, type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True, ) for i, name in enumerate(feed_target_names): if not global_block.has_var(name): raise ValueError( f"The feeded_var_names[{i}]: '{name}' doesn't exist in pruned inference program. " f"Please check whether '{name}' is a valid feed_var name, or remove it from feeded_var_names " f"if '{name}' is not involved in the target_vars calculation." ) out = global_block.var(name) global_block._prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, attrs={'col': i}, ) def append_fetch_ops( inference_program: Program, fetch_target_names: Sequence[str], fetch_holder_name: str = 'fetch', ) -> None: global_block = inference_program.global_block() fetch_var = global_block.create_var( name=fetch_holder_name, type=core.VarDesc.VarType.FETCH_LIST, persistable=True, ) for i, name in enumerate(fetch_target_names): global_block.append_op( type='fetch', inputs={'X': [name]}, outputs={'Out': [fetch_var]}, attrs={'col': i}, ) def normalize_program( program: Program, feed_vars: Tensor | list[Tensor], fetch_vars: Tensor | list[Tensor], **kwargs: Unpack[_NormalizeProgramKwargs], ) -> Program: """ Normalize/Optimize a program according to feed_vars and fetch_vars. Args: program(Program): Specify a program you want to optimize. feed_vars(Tensor | list[Tensor]): Variables needed by inference. fetch_vars(Tensor | list[Tensor]): Variables returned by inference. kwargs: Supported keys including ``skip_prune_program``. - skip_prune_program(bool): whether to skip pruning program. Defaults to False. Returns: Program: Normalized/Optimized program. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # normalize main program. >>> program = paddle.static.default_main_program() >>> normalized_program = paddle.static.normalize_program(program, [image], [predict]) """ if in_pir_mode(): return normalize_pir_program(program, feed_vars, fetch_vars, **kwargs) if not isinstance(program, Program): raise TypeError( f"program type must be `base.Program`, but received `{type(program)}`" ) if not isinstance(feed_vars, list): feed_vars = [feed_vars] if not all(isinstance(v, Variable) for v in feed_vars): raise TypeError( "feed_vars type must be a Variable or a list of Variable." ) if not isinstance(fetch_vars, list): fetch_vars = [fetch_vars] if not all(isinstance(v, Variable) for v in fetch_vars): raise TypeError( "fetch_vars type must be a Variable or a list of Variable." ) if len(program.global_block().ops) == 0: raise ValueError( "program must not be empty. at least one operator is required!" ) # remind users to set auc_states to 0 if auc op were found. for op in program.global_block().ops: # clear device of Op device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName() op._set_attr(device_attr_name, "") if op.type == 'auc': warnings.warn( "Be sure that you have set auc states to 0 before saving inference model." ) break # serialize program copy_program = program.clone() global_block = copy_program.global_block() remove_op_idx = [] for i, op in enumerate(global_block.ops): op.desc.set_is_target(False) if op.type == "feed" or op.type == "fetch": remove_op_idx.append(i) if op.type == "pylayer": sub_blocks_ids = op._blocks_attr_ids("blocks") if len(sub_blocks_ids) > 1: # pylayer op ``blocks`` attr contains forward block id and backward block id backward_block_id = sub_blocks_ids[-1] # remove backward block copy_program.blocks.pop(backward_block_id) # update attrs ``blocks`` reserved_blocks = [] for block_id in sub_blocks_ids[:-1]: reserved_blocks.append(copy_program.block(block_id)) op._update_desc_attr("blocks", reserved_blocks) for idx in remove_op_idx[::-1]: global_block._remove_op(idx) copy_program.desc.flush() feed_var_names = [var.name for var in feed_vars] skip_prune_program = kwargs.get('skip_prune_program', False) if not skip_prune_program: copy_program = copy_program._prune_with_input( feeded_var_names=feed_var_names, targets=fetch_vars ) copy_program = copy_program._inference_optimize(prune_read_op=True) fetch_var_names = [var.name for var in fetch_vars] prepend_feed_ops(copy_program, feed_var_names) append_fetch_ops(copy_program, fetch_var_names) copy_program.desc._set_version() return copy_program @static_only def serialize_program( feed_vars: Tensor | list[Tensor], fetch_vars: Tensor | list[Tensor], **kwargs: Unpack[_SerializeProgramKwargs], ) -> bytes: """ Serialize default main program according to feed_vars and fetch_vars. Args: feed_vars(Tensor | list[Tensor]): Tensor needed by inference. fetch_vars(Tensor | list[Tensor]): Tensor returned by inference. kwargs: Supported keys including ``program``. Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. - legacy_format(bool): whether to save inference program in legacy format. Defaults to False. Returns: bytes: serialized program. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # serialize the default main program to bytes. >>> serialized_program = paddle.static.serialize_program([image], [predict]) # deserialize bytes to program >>> deserialized_program = paddle.static.deserialize_program(serialized_program) """ # verify feed_vars _check_vars('feed_vars', feed_vars) # verify fetch_vars _check_vars('fetch_vars', fetch_vars) program = _get_valid_program(kwargs.get('program', None)) program = normalize_program(program, feed_vars, fetch_vars) legacy_format = kwargs.get('legacy_format', False) return _serialize_program(program, legacy_format=legacy_format) def _serialize_program(program: Program, legacy_format: bool = False) -> bytes: """ serialize given program to bytes. """ return program.desc.serialize_to_string(legacy_format=legacy_format) @static_only def serialize_persistables( feed_vars: Tensor | list[Tensor], fetch_vars: Tensor | list[Tensor], executor: Executor, **kwargs: Unpack[_SerializePersistablesKwargs], ) -> bytes: """ Serialize parameters using given executor and default main program according to feed_vars and fetch_vars. Args: feed_vars(Tensor | list[Tensor]): Tensor needed by inference. fetch_vars(Tensor | list[Tensor]): Tensor returned by inference. kwargs: Supported keys including ``program``. Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. Returns: bytes: serialized program. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # serialize parameters to bytes. >>> serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # deserialize bytes to parameters. >>> main_program = paddle.static.default_main_program() >>> deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe) """ # verify feed_vars _check_vars('feed_vars', feed_vars) # verify fetch_vars _check_vars('fetch_vars', fetch_vars) program = _get_valid_program(kwargs.get('program', None)) program = normalize_program(program, feed_vars, fetch_vars) return _serialize_persistables(program, executor) def _serialize_persistables(program: Program, executor: Executor) -> bytes: """ Serialize parameters using given program and executor. """ vars_ = list(filter(is_persistable, program.list_vars())) # warn if no variable found in model if len(vars_) == 0: warnings.warn( "no variable in your model, please ensure there are any " "variables in your model to save" ) return None # create a new program and clone persistable vars to it save_program = Program() save_block = save_program.global_block() save_var_map = {} for var in vars_: if var.type != core.VarDesc.VarType.RAW: var_copy = _clone_var_in_block(save_block, var) save_var_map[var_copy.name] = var # create in_vars and out_var, then append a save_combine op to save_program in_vars = [] for name in sorted(save_var_map.keys()): in_vars.append(save_var_map[name]) out_var_name = unique_name.generate("out_var") out_var = save_block.create_var( type=core.VarDesc.VarType.RAW, name=out_var_name ) out_var.desc.set_persistable(True) save_block.append_op( type='save_combine', inputs={'X': in_vars}, outputs={'Y': out_var}, attrs={'file_path': '', 'save_to_memory': True}, ) # run save_program to save vars # NOTE(zhiqiu): save op will add variable kLookupTablePath to save_program.desc, # which leads to diff between save_program and its desc. Call _sync_with_cpp # to keep consistency. save_program._sync_with_cpp() executor.run(save_program) # return serialized bytes in out_var return global_scope().find_var(out_var_name).get_bytes() def save_to_file(path: str, content: bytes) -> None: """ Save content to given path. Args: path(str): Path to write content to. content(bytes): Content to write. Returns: None Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # 用户自定义网络，此处用 softmax 回归为例。 >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # 序列化参数 >>> serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # 将序列化之后的参数保存到文件 >>> params_path = path_prefix + ".params" >>> paddle.static.save_to_file(params_path, serialized_params) """ if not isinstance(content, bytes): raise ValueError("'content' type should be bytes.") with open(path, "wb") as f: f.write(content) @static_only def save_inference_model( path_prefix: str, feed_vars: Tensor | list[Tensor], fetch_vars: Tensor | list[Tensor], executor: Executor, **kwargs: Unpack[_SaveInferenceModelKwargs], ) -> None: """ Save current model and its parameters to given path. i.e. Given ``path_prefix = "PATH/modelname"``, after invoking ``save_inference_model(path_prefix, feed_vars, fetch_vars, executor)``, you will find two files named ``modelname.pdmodel`` and ``modelname.pdiparams`` under ``PATH``, which represent your model and parameters respectively. Args: path_prefix(str): Directory path to save model + model name without suffix. feed_vars(Tensor | list[Tensor]): Variables needed by inference. fetch_vars(Tensor | list[Tensor]): Variables returned by inference. executor(Executor): The executor that saves the inference model. You can refer to :ref:`api_guide_executor_en` for more details. kwargs: Supported keys including 'program' and "clip_extra". Attention please, kwargs is used for backward compatibility mainly. - program(Program): specify a program if you don't want to use default main program. - clip_extra(bool): the flag indicating whether to clip extra information for every operator. Default: True. - legacy_format(bool): whether to save inference model in legacy format. Default: False. Returns: None Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # Feed data and train process # Save inference model. Note we don't save label and loss in this example >>> paddle.static.save_inference_model(path_prefix, [image], [predict], exe) # In this example, the save_inference_mode inference will prune the default # main program according to the network's input node (img) and output node(predict). # The pruned inference program is going to be saved in file "./infer_model.pdmodel" # and parameters are going to be saved in file "./infer_model.pdiparams". """ if in_pir_mode(): save_inference_model_pir( path_prefix, feed_vars, fetch_vars, executor, **kwargs ) return # check path_prefix, set model_path and params_path path_prefix = _normalize_path_prefix(path_prefix) try: # mkdir may conflict if pserver and trainer are running on the same machine dirname = os.path.dirname(path_prefix) os.makedirs(dirname) except OSError as e: if e.errno != errno.EEXIST: raise model_path = path_prefix + ".pdmodel" params_path = path_prefix + ".pdiparams" if os.path.isdir(model_path): raise ValueError(f"'{model_path}' is an existing directory.") if os.path.isdir(params_path): raise ValueError(f"'{params_path}' is an existing directory.") # verify feed_vars _check_vars('feed_vars', feed_vars) # verify fetch_vars _check_vars('fetch_vars', fetch_vars) program = _get_valid_program(kwargs.get('program', None)) # do type promotion program = process_type_promotion(program) clip_extra = kwargs.get('clip_extra', True) # serialize and save program program = normalize_program( program, feed_vars, fetch_vars, skip_prune_program=kwargs.get('skip_prune_program', False), ) legacy_format = kwargs.get('legacy_format', False) program_bytes = _serialize_program( program._remove_training_info(clip_extra=clip_extra), legacy_format=legacy_format, ) save_to_file( ( os.path.join(os.path.dirname(model_path), "__model__") if kwargs.get('separate_parameters', False) else model_path ), program_bytes, ) vars = list(filter(is_persistable, program.list_vars())) if len(list(vars)) == 0: warnings.warn( "no variable in your model, please ensure there are any variables in your model to save" ) if len(vars) > 0: save_dirname = os.path.dirname(params_path) params_filename = os.path.basename(params_path) save_vars( executor, dirname=save_dirname, main_program=program, predicate=is_persistable, filename=( None if kwargs.get('separate_parameters', False) else params_filename ), ) @static_only def deserialize_program(data: bytes) -> Program: """ Deserialize given data to a program. Args: data(bytes): serialized program. Returns: Program: deserialized program. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # serialize the default main program to bytes. >>> serialized_program = paddle.static.serialize_program([image], [predict]) # deserialize bytes to program >>> deserialized_program = paddle.static.deserialize_program(serialized_program) """ program = Program.parse_from_string(data) if not core._is_program_version_supported(program._version()): raise ValueError(f"Unsupported program version: {program._version()}\n") return program # NOTE(liuyuanle): Due to load from memory, deserialize_persistables does not support loading weights with file sizes exceeding 2GB. @static_only def deserialize_persistables( program: Program, data: bytes, executor: Executor ) -> Program: """ Deserialize given data to parameters according to given program and executor. Args: program(Program): program that contains parameter names (to deserialize). data(bytes): serialized parameters. executor(Executor): executor used to run load op. Returns: Program: deserialized program. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # User defined network, here a softmax regression example >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # serialize parameters to bytes. >>> serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # deserialize bytes to parameters. >>> main_program = paddle.static.default_main_program() >>> deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe) """ if not isinstance(program, Program): raise TypeError( f"program type must be `base.Program`, but received `{type(program)}`" ) # load params to a tmp program load_program = Program() load_block = load_program.global_block() vars_ = list(filter(is_persistable, program.list_vars())) origin_shape_map = {} load_var_map = {} check_vars = [] sparse_vars = [] for var in vars_: assert isinstance(var, Variable) if var.type == core.VarDesc.VarType.RAW: continue if isinstance(var, Parameter): origin_shape_map[var.name] = tuple(var.desc.get_shape()) if var.type == core.VarDesc.VarType.SELECTED_ROWS: sparse_vars.append(var) continue var_copy = _clone_var_in_block(load_block, var) check_vars.append(var) load_var_map[var_copy.name] = var_copy if data is None: assert len(origin_shape_map) == 0, ( "Required 'data' shall be not None if program contains parameter, but received 'data' is None." ) return # append load_combine op to load parameters, load_var_list = [] for name in sorted(load_var_map.keys()): load_var_list.append(load_var_map[name]) load_block.append_op( type='load_combine', inputs={}, outputs={"Out": load_var_list}, # if load from memory, file_path is data attrs={'file_path': data, 'model_from_memory': True}, ) executor.run(load_program) # check var shape for var in check_vars: if not isinstance(var, Parameter): continue var_tmp = paddle.base.global_scope().find_var(var.name) assert var_tmp is not None, "can't not find var: " + var.name new_shape = (np.array(var_tmp.get_tensor())).shape assert var.name in origin_shape_map, var.name + " MUST in var list." origin_shape = origin_shape_map.get(var.name) if new_shape != origin_shape: raise RuntimeError( f"Shape mismatch, program needs a parameter with shape ({origin_shape}), " f"but the loaded parameter ('{var.name}') has a shape of ({new_shape})." ) def load_from_file(path: str) -> bytes: """ Load file in binary mode. Args: path(str): Path of an existed file. Returns: bytes: Content of file. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> path_prefix = "./infer_model" # 用户自定义网络，此处用 softmax 回归为例。 >>> image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32') >>> label = paddle.static.data(name='label', shape=[None, 1], dtype='int64') >>> predict = paddle.static.nn.fc(image, 10, activation='softmax') >>> loss = paddle.nn.functional.cross_entropy(predict, label) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(paddle.static.default_startup_program()) # 序列化参数 >>> serialized_params = paddle.static.serialize_persistables([image], [predict], exe) # 将序列化之后的参数保存到文件 >>> params_path = path_prefix + ".params" >>> paddle.static.save_to_file(params_path, serialized_params) # 从文件加载序列化之后的参数 >>> serialized_params_copy = paddle.static.load_from_file(params_path) """ with open(path, 'rb') as f: data = f.read() return data @static_only def load_inference_model( path_prefix: str | None, executor: Executor, **kwargs: Unpack[_LoadInferenceModelKwargs], ) -> list[Program | list[str] | list[Tensor]]: """ Load inference model from a given path. By this API, you can get the model structure(Inference Program) and model parameters. Args: path_prefix(str | None): One of the following: - Directory path to save model + model name without suffix. - Set to None when reading the model from memory. executor(Executor): The executor to run for loading inference model. See :ref:`api_guide_executor_en` for more details about it. kwargs: Supported keys including 'model_filename', 'params_filename'. Attention please, kwargs is used for backward compatibility mainly. - model_filename(str): specify model_filename if you don't want to use default name. - params_filename(str): specify params_filename if you don't want to use default name. Returns: list: The return of this API is a list with three elements: (program, feed_target_names, fetch_targets). The `program` is a ``Program`` (refer to :ref:`api_guide_Program_en`), which is used for inference. The `feed_target_names` is a list of ``str``, which contains names of variables that need to feed data in the inference program. The `fetch_targets` is a list of ``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which we can get inference results. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> paddle.enable_static() # Build the model >>> startup_prog = paddle.static.default_startup_program() >>> main_prog = paddle.static.default_main_program() >>> with paddle.static.program_guard(main_prog, startup_prog): ... image = paddle.static.data(name="img", shape=[64, 784]) ... w = paddle.create_parameter(shape=[784, 200], dtype='float32') ... b = paddle.create_parameter(shape=[200], dtype='float32') ... hidden_w = paddle.matmul(x=image, y=w) ... hidden_b = paddle.add(hidden_w, b) >>> exe = paddle.static.Executor(paddle.CPUPlace()) >>> exe.run(startup_prog) # Save the inference model >>> path_prefix = "./infer_model" >>> paddle.static.save_inference_model(path_prefix, [image], [hidden_b], exe) >>> [inference_program, feed_target_names, fetch_targets] = ( ... paddle.static.load_inference_model(path_prefix, exe)) >>> tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32) >>> results = exe.run(inference_program, ... feed={feed_target_names[0]: tensor_img}, ... fetch_list=fetch_targets) # In this example, the inference program was saved in file # "./infer_model.pdmodel" and parameters were saved in file # " ./infer_model.pdiparams". # By the inference program, feed_target_names and # fetch_targets, we can use an executor to run the inference # program to get the inference result. """ if in_pir_mode(): return load_inference_model_pir(path_prefix, executor, **kwargs) # check kwargs supported_args = ('model_filename', 'params_filename') deprecated_args = ('pserver_endpoints',) caller = inspect.currentframe().f_code.co_name _check_args(caller, kwargs, supported_args, deprecated_args) # load from memory if path_prefix is None: _logger.warning( "Load inference model from memory is deprecated. Please specify path_prefix." ) model_filename = kwargs.get('model_filename', None) params_filename = kwargs.get('params_filename', None) if params_filename is None: raise ValueError( "params_filename cannot be None when path_prefix is None." ) program_bytes = model_filename # deserialize bytes to program program = deserialize_program(program_bytes) # do type promotion program = process_type_promotion(program) vars = list(filter(is_persistable, program.list_vars())) if len(vars) > 0: load_vars( executor, # load from memory, dirname is None dirname=None, main_program=program, predicate=is_persistable, filename=params_filename, ) # load from file else: # check and norm path_prefix path_prefix = _normalize_path_prefix(path_prefix) dir_path = os.path.dirname(path_prefix) if not os.path.isdir(dir_path): raise ValueError(f"There is no directory named {dir_path}") # set model_path and params_path in new way, # path_prefix represents a file path without suffix in this case. if not kwargs: model_path = path_prefix + ".pdmodel" params_path = path_prefix + ".pdiparams" # set model_path and params_path in old way for compatible, # path_prefix represents a directory path. else: model_filename = kwargs.get('model_filename', None) params_filename = kwargs.get('params_filename', None) # set model_path if model_filename is None: model_path = os.path.join(path_prefix, "__model__") else: model_path = os.path.join( path_prefix, model_filename + ".pdmodel" ) if not os.path.exists(model_path): model_path = os.path.join(path_prefix, model_filename) # set params_path if params_filename is None: params_path = os.path.join(path_prefix, "") else: params_path = os.path.join( path_prefix, params_filename + ".pdiparams" ) if not os.path.exists(params_path): params_path = os.path.join(path_prefix, params_filename) _logger.warning( "The old way to load inference model is deprecated. Please specify path_prefix." f" model path: {model_path}, params path: {params_path}" ) program_bytes = load_from_file(model_path) # deserialize bytes to program program = deserialize_program(program_bytes) # do type promotion program = process_type_promotion(program) vars = list(filter(is_persistable, program.list_vars())) if len(vars) > 0: load_dirname = os.path.dirname(params_path) params_filename = os.path.basename(params_path) load_vars( executor, dirname=load_dirname, main_program=program, predicate=is_persistable, filename=params_filename, ) feed_target_names = program.desc.get_feed_target_names() if paddle.framework.in_pir_executor_mode(): with paddle.pir_utils.IrGuard(): program = paddle.pir.translate_to_pir(program.desc) block = program.global_block() remove_op_list = [] fetch_targets = [] for op in block.ops: if op.name() == "pd_op.feed": var_name = op.attrs()["name"] org_value = op.result(0) with block: value = paddle.static.data( name=var_name, shape=org_value.shape, dtype=org_value.dtype, ) org_value.replace_all_uses_with(value) value.get_defining_op().move_before(op) remove_op_list.append(op) for op in remove_op_list: block.remove_op(op) for op in block.ops: if op.name() == "pd_op.fetch": fetch_targets.append(op.operand_source(0)) else: fetch_target_names = program.desc.get_fetch_target_names() fetch_targets = [ program.global_block().var(name) for name in fetch_target_names ] return [program, feed_target_names, fetch_targets] @overload @dygraph_not_support def save_vars( executor: Executor, dirname: None, main_program: Program | None = ..., vars: list[Tensor] | None = ..., predicate: Callable[[Tensor], bool] | None = ..., filename: None = ..., ) -> bytes: ... @overload @dygraph_not_support def save_vars( executor: Executor, dirname: str, main_program: Program | None = ..., vars: list[Tensor] | None = ..., predicate: Callable[[Tensor], bool] | None = ..., filename: str = ..., ) -> None: ... @dygraph_not_support def save_vars( executor, dirname, main_program=None, vars=None, predicate=None, filename=None, ): """ Save specific variables in the `Program` to files. There are two ways to specify the variables to be saved: set variables in a list and assign it to the `vars`, or use the `predicate` function to select variables that make `predicate(variable) == True`. The first way has a higher priority. The `dirname` is used to specify the folder where to save variables. If you prefer to save variables in separate files in the `dirname` folder, do not set `filename`. If you prefer to save all variables in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for saving variables. dirname(str, optional): The folder where to save variables. When you need to save the parameter to the memory, set it to None. main_program(Program, optional): The program whose variables will be saved. If it is None, the default main program will be used automatically. Default: None vars(list[Variable], optional): The list contains all variables to be saved. Default: None predicate(function, optional): The function selects the variables that make `predicate(variable) == True`. Default: None filename(str, optional): If you prefer to save all variables in a single file, use `filename` to specify it. Otherwise, let `filename` be None. Default: None Returns: str: When saving parameters to a file, returns None. When saving parameters to memory, returns a binary string containing parameters. Raises: TypeError: If `main_program` is not an instance of Program nor None. Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> main_prog = static.Program() >>> startup_prog = static.Program() >>> with static.program_guard(main_prog, startup_prog): ... data = paddle.static.data(name="img", shape=[64, 784]) ... w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') ... b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') ... hidden_w = paddle.matmul(x=data, y=w) ... hidden_b = paddle.add(hidden_w, b) >>> place = static.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(startup_prog) # The first usage: use `vars` to set the saved variables. >>> var_list = [w, b] >>> path = "./my_paddle_vars" # w and b will be save in a file named "var_file". >>> paddle.static.io.save_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") # The second usage: use `predicate` to select the saved variable. >>> def name_has_fc(var): ... res = "fc" in var.name ... return res >>> param_path = "./my_paddle_model" # all variables whose names contain "fc " are saved. >>> paddle.static.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc) """ if in_pir_mode(): return save_vars_pir(dirname, main_program, vars, filename) save_to_memory = False if dirname is None and filename is None: save_to_memory = True main_program = _get_valid_program(main_program) if vars is None: return save_vars( executor, main_program=main_program, dirname=dirname, vars=list(filter(predicate, main_program.list_vars())), filename=filename, ) else: params_var_name = "saved_params" # give warning when there is no var in model if len(list(vars)) == 0: warnings.warn( "no variable in your model, please ensure there are any variables in your model to save" ) return None save_program = Program() save_block = save_program.global_block() save_var_map = {} for each_var in vars: # NOTE: don't save the variable which type is RAW if each_var.type == core.VarDesc.VarType.RAW: continue new_var = _clone_var_in_block_(save_block, each_var) if filename is None and save_to_memory is False: save_file_path = os.path.join( os.path.normpath(dirname), new_var.name ) save_block.append_op( type='save', inputs={'X': [new_var]}, outputs={}, attrs={'file_path': os.path.normpath(save_file_path)}, ) else: save_var_map[new_var.name] = new_var if filename is not None or save_to_memory: save_var_list = [] for name in sorted(save_var_map.keys()): save_var_list.append(save_var_map[name]) save_path = '' if save_to_memory is False: save_path = os.path.join(os.path.normpath(dirname), filename) saved_params = save_block.create_var( type=core.VarDesc.VarType.RAW, name=params_var_name ) saved_params.desc.set_persistable(True) save_block.append_op( type='save_combine', inputs={'X': save_var_list}, outputs={'Y': saved_params}, attrs={ 'file_path': save_path, 'save_to_memory': save_to_memory, }, ) # NOTE(zhiqiu): save op will add variable kLookupTablePath in save_program.desc, # which leads to diff on save_program and its desc. Call _sync_with_cpp # to keep consistency. save_program._sync_with_cpp() # flush to root_scope executor.flush() executor.run(save_program) if save_to_memory: return global_scope().find_var(params_var_name).get_bytes() def load_vars( executor: Executor, dirname: str, main_program: Program | None = None, vars: list[Tensor] | None = None, predicate: Callable[[Tensor], bool] | None = None, filename: str | None = None, ) -> None: """ :api_attr: Static Graph This API loads variables from files by executor. There are two ways to specify the variables to be loaded: the first way, set variables in a list and assign it to the `vars`; the second way, use the `predicate` function to select variables that make `predicate(variable) == True`. The first way has a higher priority. The `dirname` is used to specify the folder where to load variables. If variables were saved in separate files in the folder `dirname`, set `filename` None. If all variables were saved in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for loading variables. dirname(str): The folder where to load the variables. main_program(Program, optional): The program whose variables will be loaded. If it is None, the default main program will be used automatically. Default: None vars(list[Variable], optional): The list that contains all variables to be loaded. Default: None predicate(function, optional): The function selects variables that make `predicate(variable) == True`. Default: None filename(str, optional): The file which saved all required variables. If variables were saved in separate files, set it to be None. Default: None Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> main_prog = static.Program() >>> startup_prog = static.Program() >>> with static.program_guard(main_prog, startup_prog): ... data = paddle.static.data(name="img", shape=[64, 784]) ... w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') ... b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') ... hidden_w = paddle.matmul(x=data, y=w) ... hidden_b = paddle.add(hidden_w, b) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(startup_prog) # The first usage: using `vars` to specify the variables. >>> path = "./my_paddle_vars" >>> var_list = [w, b] >>> paddle.static.io.save_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") >>> paddle.static.io.load_vars(executor=exe, dirname=path, vars=var_list, ... filename="vars_file") # w and b will be loaded, and they are supposed to # be saved in the same file named 'var_file' in the path "./my_paddle_vars". # The second usage: using the `predicate` function to select variables >>> param_path = "./my_paddle_model" >>> def name_has_fc(var): ... res = "fc" in var.name ... return res >>> paddle.static.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, ... vars=None, predicate=name_has_fc) >>> paddle.static.io.load_vars(executor=exe, dirname=param_path, main_program=main_prog, ... vars=None, predicate=name_has_fc) # Load All variables in the `main_program` whose name includes "fc". # And all the variables are supposed to be saved in separate files. """ if in_pir_mode(): return load_vars_pir(executor, dirname, main_program, vars, filename) vars_from_memory = False if dirname is not None: dirname = os.path.normpath(dirname) else: vars_from_memory = True if filename == '': filename = None if vars is None: if main_program is None: main_program = default_main_program() if not isinstance(main_program, Program): raise TypeError( f"The type of input main_program is invalid, expected type is base.Program, but received {type(main_program)}" ) load_vars( executor, dirname=dirname, main_program=main_program, vars=list(filter(predicate, main_program.list_vars())), filename=filename, ) else: load_prog = Program() load_block = load_prog.global_block() if main_program is None: main_program = default_main_program() if not isinstance(main_program, Program): raise TypeError( f"The type of input main_program is invalid, expected type is base.Program, but received {type(main_program)}" ) # save origin param shape orig_para_shape = {} load_var_map = {} check_vars = [] sparse_vars = [] for each_var in vars: assert isinstance(each_var, Variable) if each_var.type == core.VarDesc.VarType.RAW: continue if isinstance(each_var, Parameter): orig_para_shape[each_var.name] = tuple( each_var.desc.get_shape() ) if each_var.type == core.VarDesc.VarType.SELECTED_ROWS: sparse_vars.append(each_var) continue new_var = _clone_var_in_block_(load_block, each_var) check_vars.append(each_var) if filename is None: if dirname is None: raise ValueError( "The directory path and params cannot be None at the same time." ) load_block.append_op( type='load', inputs={}, outputs={'Out': [new_var]}, attrs={'file_path': os.path.join(dirname, new_var.name)}, ) else: load_var_map[new_var.name] = new_var for each_var in sparse_vars: assert isinstance(each_var, Variable) if filename is not None: raise ValueError( "SelectedRows can not be load with load_combine" ) new_var = _clone_var_in_block_(load_block, each_var) var_path = os.path.join(dirname, new_var.name) if not os.path.exists(var_path): raise ValueError( f"SelectedRows var {new_var.name} can not find at {var_path}" ) if os.path.isfile(var_path): load_block.append_op( type='load', inputs={}, outputs={'Out': [new_var]}, attrs={'file_path': os.path.join(dirname, new_var.name)}, ) else: blocks = [] block_paths = os.listdir(var_path) for block in block_paths: if block.startswith(new_var.name): blocks.append(block) slices = [] for block in blocks: slice = load_block.create_var( name=block, type=new_var.type, shape=new_var.shape, dtype=new_var.dtype, persistable=False, ) slices.append(slice) file_path = os.path.join(var_path, block, "Param") load_block.append_op( type='load', inputs={}, outputs={'Out': [slice]}, attrs={'file_path': file_path}, ) load_block.append_op( type='lookup_sparse_table_merge', inputs={'X': slices}, outputs={'Out': new_var}, attrs={}, ) if filename is not None: load_var_list = [] for name in sorted(load_var_map.keys()): load_var_list.append(load_var_map[name]) if vars_from_memory is False: filename = os.path.join(dirname, filename) load_block.append_op( type='load_combine', inputs={}, outputs={"Out": load_var_list}, attrs={ 'file_path': filename, 'model_from_memory': vars_from_memory, }, ) executor.run(load_prog) # check var shape for each_var in check_vars: if not isinstance(each_var, Parameter): continue var_temp = paddle.base.global_scope().find_var(each_var.name) assert var_temp is not None, "can't not find var: " + each_var.name new_shape = (np.array(var_temp.get_tensor())).shape assert each_var.name in orig_para_shape, ( each_var.name + "MUST in var list" ) orig_shape = orig_para_shape.get(each_var.name) if new_shape != orig_shape: raise RuntimeError( f"Variable's shape does not match, the Program requires a parameter with the shape of ({orig_shape}), " f"while the loaded parameter (namely [ {each_var.name} ]) has a shape of ({new_shape})." ) @static_only def save( program: Program, model_path: str, protocol: int = 4, **configs: Unpack[_SaveKwargs], ) -> None: """ This function save parameters, optimizer information and network description to model_path. The parameters contains all the trainable Tensor, will save to a file with suffix ".pdparams". The optimizer information contains all the Tensor used by optimizer. For Adam optimizer, contains beta1, beta2, momentum etc. All the information will save to a file with suffix ".pdopt". (If the optimizer have no Tensor need to save (like SGD), the fill will not generated). The network description is the description of the program. It's only used for deployment. The description will save to a file with a suffix ".pdmodel". Args: program(Program) : The program to saved. model_path(str): the file prefix to save the program. The format is "dirname/file_prefix". If file_prefix is empty str. A exception will be raised protocol(int, optional): The protocol version of pickle module must be greater than 1 and less than 5. Default: 4 configs(dict, optional) : optional keyword arguments. Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") """ if in_pir_mode(): return save_pir(program, model_path, protocol, **configs) base_name = os.path.basename(model_path) assert base_name != "", ( "The input model_path MUST be format of dirname/filename [dirname\\filename in Windows system], but received model_path is empty string." ) if 'pickle_protocol' in configs: protocol = configs['pickle_protocol'] warnings.warn( "'pickle_protocol' is a deprecated argument. Please use 'protocol' instead." ) if not isinstance(protocol, int): raise ValueError( f"The 'protocol' MUST be `int`, but received {type(protocol)}" ) if protocol < 2 or protocol > 4: raise ValueError( f"Expected 1<'protocol'<5, but received protocol={protocol}" ) dir_name = os.path.dirname(model_path) if dir_name and not os.path.exists(dir_name): os.makedirs(dir_name) def get_tensor(var): t = global_scope().find_var(var.name).get_tensor() return np.array(t) parameter_list = list(filter(is_parameter, program.list_vars())) param_dict = {p.name: get_tensor(p) for p in parameter_list} param_dict = _unpack_saved_dict(param_dict, protocol) # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3' if sys.platform == 'darwin' and sys.version_info.major == 3: pickle_bytes = pickle.dumps(param_dict, protocol=protocol) with open(model_path + ".pdparams", 'wb') as f: max_bytes = 2**30 f.writelines( pickle_bytes[i : i + max_bytes] for i in range(0, len(pickle_bytes), max_bytes) ) else: with open(model_path + ".pdparams", 'wb') as f: pickle.dump(param_dict, f, protocol=protocol) optimizer_var_list = list( filter(is_belong_to_optimizer, program.list_vars()) ) opt_dict = {p.name: get_tensor(p) for p in optimizer_var_list} with open(model_path + ".pdopt", 'wb') as f: pickle.dump(opt_dict, f, protocol=protocol) main_program = program.clone() program.desc.flush() with open(model_path + ".pdmodel", "wb") as f: f.write(program.desc.serialize_to_string()) @static_only def load( program: Program, model_path: str, executor: Executor | None = None, var_list: Sequence[Tensor] | None = None, ) -> None: """ :api_attr: Static Graph This function get parameters and optimizer information from program, and then get corresponding value from file. An exception will throw if shape or dtype of the parameters is not match. This function can also load model file saved with [ save_params, save_persistables, save_vars ]. var_list can not be None when load single model file ( filename is not None When save_params, save_persistables or save_vars is called ). Args: program(Program): The program will be loaded model_path(str): The file prefix store the program executor(Executor, optional): The executor used for initialize the parameter When startup program is not run. var_list(list|tuple, optional): The Tensor list/tuple to load single model file saved with [ save_params, save_persistables, save_vars ]. Default: None Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> static.load(prog, "./temp") """ assert executor is None or isinstance(executor, Executor) model_prefix = model_path if model_prefix.endswith(".pdparams"): model_prefix = model_prefix[:-9] elif model_prefix.endswith(".pdopt"): model_prefix = model_prefix[:-6] elif model_prefix.endswith(".pdmodel"): model_prefix = model_prefix[:-8] elif model_prefix.endswith(".json"): model_prefix = model_prefix[:-5] if in_pir_mode(): return load_pir(program, model_prefix, executor, var_list) parameter_file_name = model_prefix + ".pdparams" if not os.path.exists(parameter_file_name): # model file save by base.save not found, try to load model file saved with # [save_vars, save_params, save_persistables] _logger.debug( f"{parameter_file_name} not found, try to load model file saved with [ save_params, save_persistables, save_vars ]" ) if executor is None: raise ValueError( "executor is required when loading model file saved with [ save_params, save_persistables, save_vars ]" ) if var_list is not None: var_list_names = [var.name for var in var_list] else: var_list_names = None if os.path.isdir(model_path): binary_file_set = set() for root, dirs, files in os.walk(model_path, topdown=False): for f in files: binary_file_set.add( os.path.join(root, f).replace("\\", "/") ) program_var_list = list(program.list_vars()) loaded_var_list = [] for var in program_var_list: var_path = os.path.join(model_path, var.name).replace("\\", "/") load_condition = ( var_list_names is None or var.name in var_list_names ) if var_path in binary_file_set and load_condition: loaded_var_list.append(var) binary_file_set.remove(var_path) if len(binary_file_set) > 0: unused_var_list = " ".join(list(binary_file_set)) _logger.warning( "variable file [ {} ] not used".format( " ".join(list(binary_file_set)) ) ) try: load_vars( executor=executor, dirname=model_path, vars=loaded_var_list ) except RuntimeError as e: _logger.error(e) raise e except: raise RuntimeError( "Failed to load model file, please make sure model file is saved with the " "following APIs: save_params, save_persistables, save_vars" ) return elif os.path.isfile(model_path): if var_list is None: raise ValueError( "var_list is required when loading model file saved with [ save_params, save_persistables, save_vars ]" ) program_var_list = program.list_vars() program_var_name_set = {var.name for var in program_var_list} # check all the variable included in program for var in var_list: if var.name not in program_var_name_set: raise LookupError( "loaded var [{}] is not in program variable list" ) dir_name, file_name = os.path.split(model_path) try: load_vars( executor=executor, dirname=dir_name, vars=var_list, filename=file_name, ) except RuntimeError as e: _logger.error(e) raise e except: raise RuntimeError( "Failed to load model file , please make sure model file is saved with the " "the following APIs: [ save_params, save_persistables, save_vars ]. " "When these API called, filename CANNOT be None" ) return def set_var(var, ndarray): t = global_scope().find_var(var.name).get_tensor() p = t._place() if p.is_cpu_place(): place = paddle.base.CPUPlace() elif p.is_cuda_pinned_place(): place = paddle.base.CUDAPinnedPlace() elif p.is_xpu_place(): p = paddle.base.core.Place() p.set_place(t._place()) place = paddle.base.XPUPlace(p.xpu_device_id()) elif p.is_custom_place(): p = paddle.base.core.Place() p.set_place(t._place()) place = paddle.base.CustomPlace( paddle.device.get_device().split(':')[0], p.custom_device_id() ) else: p = paddle.base.core.Place() p.set_place(t._place()) place = paddle.base.CUDAPlace(p.gpu_device_id()) t.set(ndarray, place) parameter_list = list(filter(is_parameter, program.list_vars())) if executor: paddle.base.core._create_loaded_parameter( parameter_list, global_scope(), executor._default_executor ) with open(parameter_file_name, 'rb') as f: # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3' if sys.platform == 'darwin' and sys.version_info.major == 3: load_dict = _pickle_loads_mac(parameter_file_name, f) else: load_dict = _safe_load_pickle(f, encoding='latin1') load_dict = _pack_loaded_dict(load_dict) for v in parameter_list: assert v.name in load_dict, ( f"Can not find [{v.name}] in model file [{parameter_file_name}]" ) set_var(v, load_dict[v.name]) optimizer_var_list = list( filter(is_belong_to_optimizer, program.list_vars()) ) if len(optimizer_var_list) > 0: opt_file_name = model_prefix + ".pdopt" assert os.path.exists(opt_file_name), ( f"Optimizer file [{opt_file_name}] not exits" ) if executor: paddle.base.core._create_loaded_parameter( optimizer_var_list, global_scope(), executor._default_executor ) with open(opt_file_name, 'rb') as f: load_dict = _safe_load_pickle(f, encoding='latin1') for v in optimizer_var_list: assert v.name in load_dict, ( f"Can not find [{v.name}] in model file [{opt_file_name}]" ) set_var(v, load_dict[v.name]) @static_only def set_program_state( program: Program, state_dict: dict[str, npt.NDArray[Any]] ) -> None: """ Set program parameter from state_dict An exception will throw if shape or dtype of the parameters is not match. NOTICE: This function MUST called after run start_up_program Args: program(Program): The program to be set state_dict(dict): the dict store Parameter and optimizer information Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> program_state = static.load_program_state("./temp") >>> static.set_program_state(prog, program_state) """ state_dict = _pack_loaded_dict(state_dict) if in_pir_mode(): params, opts = get_pir_parameters(program) parameter_list = params + opts parameter_list = [var for var in parameter_list if var.persistable] else: parameter_list = list(filter(is_persistable, program.list_vars())) used_para_list = {} for para in parameter_list: var_temp = paddle.base.global_scope().find_var(para.name) assert var_temp is not None, ( f"Variable [ {para.name} ] Not found, Please make sure run startup program" ) if para.name in state_dict: # set value from state dict orig_para_np = np.array(var_temp.get_tensor()) new_para_np = state_dict[para.name] assert orig_para_np.shape == new_para_np.shape, ( f"Parameter's shape does not match, the Program requires a parameter with the shape of ({orig_para_np.shape}), " f"while the loaded parameter (namely [ {para.name} ]) has a shape of ({new_para_np.shape})." ) assert orig_para_np.dtype == new_para_np.dtype, ( f"Parameter's data type does not match, the Program requires a parameter with a dtype of ({orig_para_np.dtype}), " f"while the loaded parameter (namely [ {para.name} ]) has a dtype of ({new_para_np.dtype})." ) ten = var_temp.get_tensor() ten_place = ten._place() # assert ten_place.is_gpu_place() or ten_place.is_cpu_place(), \ # "Place not support, only support CPUPlace and GPUPlace, now is {}".format(str(ten_place)) py_place = paddle.base.CPUPlace() if ten_place.is_cuda_pinned_place(): place = paddle.base.CUDAPinnedPlace() elif ten_place.is_gpu_place(): p = paddle.base.core.Place() p.set_place(ten_place) py_place = paddle.base.CUDAPlace(p.gpu_device_id()) elif ten_place.is_xpu_place(): p = paddle.base.core.Place() p.set_place(ten_place) py_place = paddle.base.XPUPlace(p.xpu_device_id()) ten.set(new_para_np, py_place) used_para_list[para.name] = 1 unused_para_list = [] for k, v in state_dict.items(): if k not in used_para_list: unused_para_list.append(k) if len(unused_para_list) > 0: warnings.warn( "This list is not set, Because of Parameter not found in program. There are: {}".format( " ".join(unused_para_list) ) ) @dygraph_not_support def get_program_persistable_vars(program: Program) -> list[Tensor]: """ Get all the persistable vars from Program. Args: var(Program): The Program to get persistable vars Returns: list: The list contains all persistable vars in the program Examples: .. code-block:: python >>> import paddle >>> import paddle.static.io as io >>> paddle.enable_static() >>> data = paddle.static.data(name="img", shape=[64, 784]) >>> w = paddle.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') >>> b = paddle.create_parameter(shape=[200], dtype='float32', name='fc_b') >>> list_para = io.get_program_persistable_vars( paddle.static.default_main_program() ) """ return list(filter(is_persistable, program.list_vars())) def load_program_state( model_path: str, var_list: Sequence[Tensor] | None = None ) -> dict[str, npt.NDArray[Any]]: """ Load program state from local file Args: model_path(str): The file prefix store the program var_list(list|tuple, optional): The Tensor list/tuple to load saved with [ save_params, save_persistables, save_vars ]. Default: None. The var_list is only used to get name, will not be modified. Returns: state_dict(dict): the dict store Parameter and optimizer information Examples: .. code-block:: python >>> import paddle >>> import paddle.static as static >>> paddle.enable_static() >>> x = static.data(name="x", shape=[10, 10], dtype='float32') >>> y = static.nn.fc(x, 10) >>> z = static.nn.fc(y, 10) >>> place = paddle.CPUPlace() >>> exe = static.Executor(place) >>> exe.run(static.default_startup_program()) >>> prog = static.default_main_program() >>> static.save(prog, "./temp") >>> program_state = static.load_program_state("./temp") """ model_prefix = model_path if model_prefix.endswith(".pdparams"): model_prefix = model_prefix[:-9] elif model_prefix.endswith(".pdopt"): model_prefix = model_prefix[:-6] elif model_prefix.endswith(".pdmodel"): model_prefix = model_prefix[:-8] parameter_file_name = model_prefix + ".pdparams" if not os.path.exists(parameter_file_name): # model file saved with base.save is not found, try to load model file saved with # [save_vars, save_params, save_persistables] _logger.debug( f"{parameter_file_name} not found, try to load model file saved with [ save_params, save_persistables, save_vars ]" ) var_name_list = [] if var_list is None and os.path.isfile(model_path): raise ValueError( "var_list can not be None when model_path is a file type" ) for root, dirs, files in os.walk(model_path, topdown=False): for f in files: file_path = os.path.join(root, f) var_temp_name = os.path.relpath(file_path, model_path) var_temp_name = var_temp_name.replace("\\", "/") var_name_list.append(var_temp_name) with _load_program_scope(): load_prog = Program() load_block = load_prog.global_block() def clone_var_to_block(block, var): if not isinstance(var, Variable): raise TypeError("value in var_list must be variable") return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=( var.lod_level if var.desc.type() == core.VarDesc.VarType.DENSE_TENSOR else None ), persistable=True, ) def _load_vars_with_try_catch( exe, dirname, vars, filename, raise_error=True ): try: load_vars( executor=exe, dirname=dirname, vars=vars, filename=filename, ) return True except: error_str = ( "Failed to load model/variables `%s`, please make sure " "model/variables file is saved with the following APIs: " "save_params, save_persistables, save_vars." ) filenames = ( [var.name for var in vars] if filename is None else filename ) if raise_error: raise RuntimeError(error_str % filenames) else: warnings.warn(error_str % filenames, RuntimeWarning) return False place = paddle.base.CPUPlace() exe = paddle.base.Executor(place) loaded_var_list = [] if os.path.isfile(model_path): # when model_path is file, var_list cannot be None dir_name, file_name = os.path.split(model_path) for var in var_list: loaded_var_list.append(clone_var_to_block(load_block, var)) _load_vars_with_try_catch( exe, dir_name, loaded_var_list, file_name ) else: # var_list can be None or not None if var_list is not None: for var in var_list: loaded_var_list.append( clone_var_to_block(load_block, var) ) _load_vars_with_try_catch( exe, model_path, loaded_var_list, None ) else: for var_name in var_name_list: # NOTE(chenweihang): If identify which files the user wants # to load from the disk, we load these variables one by one. # If a file does not exist, we only warn the user that the # file may be an irrelevant file, but does not throw an error # to ensure that other legal variables can be loaded. temp_var = load_block.create_var( name=var_name, persistable=True ) if _load_vars_with_try_catch( exe, model_path, [temp_var], None, False ): loaded_var_list.append(temp_var) res_dict = {} for var in loaded_var_list: res_dict[var.name] = np.asarray( paddle.base.global_scope().find_var(var.name).get_tensor() ) return res_dict assert os.path.exists(parameter_file_name), ( f"Parameter file [{parameter_file_name}] not exits" ) with open(parameter_file_name, 'rb') as f: # When value of dict is lager than 4GB ,there is a Bug on 'MAC python3' if sys.platform == 'darwin' and sys.version_info.major == 3: para_dict = _pickle_loads_mac(parameter_file_name, f) else: para_dict = _safe_load_pickle(f, encoding='latin1') para_dict = _pack_loaded_dict(para_dict) opt_file_name = model_prefix + ".pdopt" if os.path.exists(opt_file_name): with open(opt_file_name, 'rb') as f: opti_dict = _safe_load_pickle(f, encoding='latin1') para_dict.update(opti_dict) return para_dict